id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-337256-b3j3kg73	Wang, Peipei	Prediction of Epidemic Trends in COVID-19 with Logistic Model and Machine Learning Technics	2020-07-01		.txt	text/plain	1944	114	60	title: Prediction of Epidemic Trends in COVID-19 with Logistic Model and Machine Learning Technics We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into Fbprophet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Many scholars have developed a number of predicting methods for the trend forecasting of COVID-19, in some severe countries and global [8, 9] , debating 30 about mathematical model, infectious disease model, and artificial intelligence model. The models based on mathematical statistics, machine learning and deep learning have been applied to the prediction of time series of epidemic development [10, 11] . Generalized logistic growth modeling of the covid-19 outbreak in 29 provinces in china and in the rest of the world	./cache/cord-337256-b3j3kg73.txt	./txt/cord-337256-b3j3kg73.txt